(-F)-生成规律与系统规律识别

一个系统(控制系统、通讯系统)没有受到某个规律的干扰(或攻击),则系统处于稳定运动状态或者按预定的规律运动.未知规律进入到系统中,对系统进行干扰,使得系统的运动规律紊乱.利用函数单向S-粗集对偶(dual of function one direction singular rough sets),给出系统中(-F)-生成规律的概念,给出(-F)-生成规律的生成模型和对系统规律的识别.函数单向S-粗集对偶是识别系统中存在干扰规律,识别系统规律的一个新的理论与方法.     

■-生成规律与系统规律识别

一个系统(控制系统、通讯系统)没有受到某个规律的干扰(或攻击),则系统处于稳定运动状态或者按预定的规律运动。未知规律进入到系统中,对系统进行干扰,使得系统的运动规律紊乱。利用函数单向S-粗集对偶(dual of function one direction singular rough sets),给出系统中■-生成规律的概念,给出■-生成规律的生成模型和对系统规律的识别。函数单向S-粗集对偶是识别系统中存在干扰规律,识别系统规律的一个新的理论与方法。     

函数S-粗集与粗规律挖掘-分离

研究了函数S-粗集的本质和特性,及其用于挖掘系统的潜在规律。利用函数S-粗集(单向函数S-粗集,双向函数S-粗集)的数学结构和特性,给出系统中粗规律模型的生成方法。给出了利用函数S-粗集进行系统中的粗规律挖掘与分离的讨论,和系统中粗规律分离的应用。例子证明,函数S-粗集是系统规律挖掘的一个有力工具。     

规律与它的f-属性控制

函数单向S-粗集对偶(dual of function one direction singular rough set),具有单向动态特性和规律特性;它是函数S-粗集(function singular rough set)的基本形式之一。函数S-粗集是在改进S-粗集的基础上提出的。利用函数单向S-粗集对偶的动态特性和规律特性,给出f·-规律,f·-规律的属性特征,属性距离,f·-冗余规律概念。利用这些概念,提出规律与它的f·-属性控制,并给出f·-属性控制定理,f·-属性控制判定定理,f·-属性控制识别准则与应用。     

F-规律推理与规律挖掘

对S-粗集给出改进,把函数这个分析工具引入到S-粗集中,提出函数S-粗集(function singularrough sets)。函数单向S-粗集(function one direction singular rough sets)是函数S-粗集的基本形式之一,它是以R-函数等价类[u]定义的;ui∈[u]是一个函数,函数是一个规律。函数单向S-粗集具有单向动态特性与规律特征:利用函数单向S-粗集的规律特征,给出F-规律推理与F-规律推理的规律挖掘概念,提出F-规律推理的规律挖掘定理,F-规律推理的规律挖掘原理与F-规律推理的规律挖掘应用。F-规律推理的规律挖掘是寻找系统中未知规律研究的一个新的研究方向。     

一种基于函数S-粗集的态势预测方法

针对态势评估中未来可能出现态势的优劣情况,提出了一种基于函数S-粗集的态势预测方法。利用函数S-粗集的粗规律挖掘功能,采用拉格朗日插值多项式,以函数单向S-粗集对偶为例,给出了应用该方法的具体步骤。最后通过态势评估中一个具体实例证明了该方法的有效性。得到了运用函数S-粗集能够找到隐藏在系统中的粗规律的结论。     

单向S-粗决策规律与决策规律挖掘

利用单向S-粗集,给出单向S-粗决策规律生成方法;给出上决策规律,下决策规律,单向S-粗决策规律核,单向S-粗决策规律带,单向S-粗决策规律壳的概念;利用这些概念,提出下决策规律传递定理,上决策规律传递定理,F-分离的属性定理,粗决策规律挖掘定理,与粗决策规律挖掘准则。     

■-风险属性入侵与承灾规律识别

针对公共突发事件的动态性、不确定性和危害性,利用单向S-粗集对偶(dual of one direction singular rough sets)给出承灾类、承灾量、■-承灾损失、■-承灾度、■-承灾损失规律、■-承灾度规律的概念,利用这些概念提出了■-承灾损失与■-承灾度关系定理,■-承灾损失规律与■-承灾度规律关系定理,■-风险属性入侵规律分辨定理,■-承灾度规律识别准则与应用。单向S-粗集是应急管理理论与方法研究中的一个新的有效的数学工具。     

单向S-粗集对偶与数据筛选-过滤的扩展应用

S-粗集(singular rough sets)是对Z.Pawlak粗集的改进,单向S-粗集对偶(dual of one direction sin-gular rough sets)是S-粗集的基本形式之一。利用单向S-粗集对偶,给出数据属性,数据筛选-过滤概念,数据筛选-过滤序定理,合成数据筛选-过滤定理,及数据筛选-过滤准则。利用这些结果,给出应用。单向S-粗集对偶是动态数据筛选-过滤研究的一个新工具。     

(F)-风险属性入侵与承灾规律识别

针对公共突发事件的动态性、不确定性和危害性,利用单向S-粗集对偶(dual ofone direction sin-gular rough sets)给出承灾类、承灾量、F-承灾损失、F-承灾度、F-承灾损失规律、F-承灾度规律的概念,利用这些概念提出了F-承灾损失与F-承灾度关系定理,F-承灾损失规律与F-承灾度规律关系定理,F-风险属性入侵规律分辨定理,F-承灾度规律识别准则与应用.单向S-粗集是应急管理理论与方法研究中的一个新的有效的数学工具.     

F-generation law and recognition of system law

If a system is not disturbed (or invaded) by some law, there is no doubt that each system will move according to the expected law and keep stable. Although such a fact often appears, some unknown law breaks into the system and leads it into turbulence. Using function one direction S-rough sets, this article gives the concept of the F-generation law in the system, the generation model of the F-generation law and the recognition method of the system law. Function one direction singular rough sets is a new theory and method in recognizing the disturbance law existing in the system and recognizing the system law.     

F-generation law and recognition of system law

If a system is not disturbed (or invaded) by some law, there is no doubt that each system will move according to the expected law and keep stable. Although such a fact often appears, some unknown law breaks into the system and leads it into turbulence. Using function one direction S-rough sets, this article gives the concept of the F -generation law in the system, the generation model of the F -generation law and the recognition method of the system law. Function one direction singular rough sets is a new theory and method in recognizing the disturbance law existing in the system and recognizing the system law.     

F−rough law and the discovery of rough law

By using function one direction S-rough sets (function one direction singular rough sets), this article presents the concepts of F-law, F-rough law, and the relation metric of rough law; by using these concepts, this article puts forward the theorem of F-law relation metric, two orders theorem of F-rough law relation metric, the attribute theorem of F-rough law band, the extremum theorem of F-rough law relation metric, the discovery principle of F-rough law and the application of F-rough law.     

Rough law energy and measurement of ¯F-decomposition rough law

By employing function one-direction S-rough sets and rough law generation method based on function S-rough sets, ¯ f-decomposition law and ¯ F-decomposition rough law are proposed, and the measurement of rough law variation in the process of rough law ¯ F-decomposition is researched. The concepts of law energy and attribute ¯ f-interference degree are presented, which make the variation of rough law become measurable. ¯ f-decomposition law energy characteristic theorem, ¯ fdecomposition law energy inequality theorem, ¯ F-decomposition rough law energy characteristic theorem, and ¯ f-decomposition law energy mean value theorem are presented.     

Rough law generation and its separation-recognition

By using function S-rough sets (function singular rough sets), this paper gives rough law generation and the theorem of rough law generation. Based on these results above, the paper proposes rough law separation, the theorem of rough law separation, the compound generation theorem of rough law bands, and the principle of rough law bands. In the end, an application of rough law separation in recognizing the risk law of profit is presented.     

Q-learning强化学习制导律

在未来的战场中,智能导弹将成为精确有效的打击武器,导弹智能化已成为一种主要的发展趋势。本文以传统的比例制导律为基础,提出基于强化学习的变比例系数制导算法。该算法以视线转率作为状态,依据脱靶量设计奖励函数,并设计离散化的行为空间,为导弹选择正确的制导指令。实验仿真验证了所提算法比传统的比例制导律拥有更好的制导精度,并使导弹拥有了自主决策能力。     

VSCMG簇的操纵律优化设计

变速控制力矩陀螺(variable speed control moment gyro,VSCMG)簇相对于单框架控制力矩陀螺簇仅增加了飞轮转速可调自由度,实现难度不大,但能够缓解奇异问题。基于线性代数理论,从机理上分析了已有的添加零运动的加权伪逆操纵律不能规避VSCMG簇内所有奇异点。针对添加零运动的加权伪逆操纵律不能规避奇异点的问题,采用优化方法,设计出一种新型的VSCMG簇操纵律,能够规避采用传统添加零运动的加权伪逆操纵律不能规避的奇异点。最后搭建整个航天器姿态控制系统,仿真验证了所设计的新型操纵律奇异规避的有效性。